estimation of quantity and harvesttiming of the mango crop

A forward estimate of mango fruit harvest volume and scheduling is required for farm management, for organization in terms of labour planning and market sales. Harvest timing estimation in mango production is currently achieved using accumulated growing degree days (GDD) from from early stages of flower development, non-destructive estimates of fruit dry matter content by handheld near infra-red spectrometry, and destructive assessment of internal flesh colour. For fruit load estimation, current best practice involves manual counting of total fruit per tree. A range of technologies are becoming available that have relevance to assessment of mango crop harvest timing and fruit load forecast. Four activities were undertaken to assess relevant technologies: (i) A hardware system based on LoRa connected temperature sensors was characterised and recommended for field use based on measurement accuracy, battery life and reception range. An alternative algorithm on GDD calculation involving use of a function that penalises high temperatures as well as low temperatures was demonstrated to better predict harvest maturity in warmer climates. Required heat units (GDD, Tb = 12 °C, TB =32 °C) to achieve maturity were documented as 2185, 1728 and 1740 for the cultivars Keitt, Calypso, and Honey Gold, respectively. (ii) Vis-NIR spectrometry was trialled for non-invasive assessment of fruit flesh colour in the context of harvest maturity estimation, using a data set of 2034 spectra from 19 populations, where a population is an orchard/season/flowering event. The best leave-one-out-population cross validation prediction result was obtained using a Support Vector Regression (R2 of 0.63 and RMSEP of 5.52 on CIE B). However, this performance was inadequate for recommendation for use in non-invasive assessment of fruit maturity, which requires estimation to within 2.0 CIE B units. (iii) A procedure for prediction of fruit size at harvest based on measurements made prior to harvest was established, based a linear growth model for weight increment. The procedure was demonstrated for Honey Gold, Calypso and Keitt populations, with estimation error of 8.64 ± 13.7% and 0.61 ± 4.7% for measurements made between either five and four, or four and three weeks before harvest, respectively. (iv) A procedure for use of in-field machine vision-based count of fruit on tree in estimation of orchard fruit load was established, based on use of imaging on two dates to capture fruit arising from different flowering events. The two imaging estimations were accurate estimates of total orchard fruit load as measured by packhouse count, with R2 of 0.98 and slope of 0.99 across six orchards. These four activities demonstrate the potential of new technologies for improved estimation of harvest timing and load.

农场管理需开展芒果果实收获量与采收日程的前瞻性预估，以统筹劳动力规划与市场销售工作。当前芒果生产中的收获时机预估，主要通过开花早期的积温（Growing Degree Days, GDD）、手持近红外光谱法无损估算果实干物质含量，以及破坏性检测果肉内部色泽来完成。而果园果实负载量估算的现行最佳实践，仍为人工逐树统计总果实数量。目前已有一批相关技术可用于芒果收获时机评估与果实负载量预测。本研究开展了四项活动以评估相关技术：(i) 对基于LoRa连接的温度传感器硬件系统进行了性能表征，并综合测量精度、电池续航与接收距离等指标，推荐其用于田间部署。此外，一款兼顾高温与低温惩罚项的GDD计算改进算法，被证实可在温热气候下更精准地预测果实采收成熟度。本研究明确了三个芒果品种达到采收成熟度所需的积温单位（GDD，基准低温Tb=12℃，基准高温TB=32℃）：Keitt为2185，Calypso为1728，Honey Gold为1740。(ii) 针对采收成熟度估算场景，本研究试验了可见光-近红外光谱（Visible-Near Infrared Spectrometry, Vis-NIR）法用于非侵入式评估果肉色泽，试验所用数据集包含来自19个群体的2034条光谱——每个群体对应一个果园、季次与开花事件的组合。其中，最优的留一群体交叉验证预测结果由支持向量回归（Support Vector Regression, SVR）模型得到，在CIE B通道上的R²为0.63，均方根预测误差（Root Mean Squared Error of Prediction, RMSEP）为5.52。但该性能仍无法满足非侵入式果实成熟度评估的应用要求——此类应用需将预估误差控制在2.0个CIE B单位以内。(iii) 基于果实重量增长的线性生长模型，本研究建立了一套通过采收前测量数据预估采收时果实大小的流程。该流程已在Honey Gold、Calypso与Keitt三个品种的群体中得到验证：若在采收前5至4周、或4至3周开展测量，预估误差分别为8.64±13.7%与0.61±4.7%。(iv) 基于两次成像以捕获不同开花事件所结果实的方案，本研究建立了一套基于田间机器视觉计数树上果实的流程，用于估算果园果实负载量。在6个果园的验证中，两次成像得到的果实负载量估算值与包装厂人工统计的总果实数吻合度极佳，R²达0.98，拟合斜率为0.99。上述四项研究活动证实了新兴技术在优化芒果收获时机与负载量预估方面的应用潜力。